Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 22.214.171.124 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors.
avian influenza; spatial epidemiology; H5N1; Chicken
Chewing lice (Phthiraptera) that parasitize the globally threatened swan goose Anser cygnoides have been long recognized since the early 19th century, but those records were probably biased towards sampling of captive or domestic geese due to the small population size and limited distribution of its wild hosts. To better understand the lice species parasitizing swan geese that are endemic to East Asia, we collected chewing lice from 14 wild geese caught at 3 lakes in northeastern Mongolia. The lice were morphologically identified as 16 Trinoton anserinum (Fabricius, 1805), 11 Ornithobius domesticus Arnold, 2005, and 1 Anaticola anseris (Linnaeus, 1758). These species are known from other geese and swans, but all of them were new to the swan goose. This result also indicates no overlap in lice species between older records and our findings from wild birds. Thus, ectoparasites collected from domestic or captive animals may provide biased information on the occurrence, prevalence, host selection, and host-ectoparasite interactions from those on wild hosts.
Trinoton anserinum; Ornithobius domesticus; Anaticola anseris; chewing louse; swan goose
Population connectivity is an important consideration in studies of disease transmission and biological conservation, especially with regard to migratory species. Determining how and when different subpopulations intermingle during different phases of the annual cycle can help identify important geographical regions or features as targets for conservation efforts and can help inform our understanding of continental-scale disease transmission. In this study, stable isotopes of hydrogen and carbon in contour feathers were used to assess the degree of molt-site fidelity among Bar-headed Geese (Anser indicus) captured in north-central Mongolia. Samples were collected from actively molting Bar-headed Geese (n = 61), and some individual samples included both a newly grown feather (still in sheath) and an old, worn feather from the bird’s previous molt (n = 21). Although there was no difference in mean hydrogen isotope ratios for the old and new feathers, the isotopic variance in old feathers was approximately three times higher than that of the new feathers, which suggests that these birds use different and geographically distant molting locations from year to year. To further test this conclusion, online data and modeling tools from the isoMAP website were used to generate probability landscapes for the origin of each feather. Likely molting locations were much more widespread for old feathers than for new feathers, which supports the prospect of low molt-site fidelity. This finding indicates that population connectivity would be greater than expected based on data from a single annual cycle, and that disease spread can be rapid even in areas like Mongolia where Bar-headed Geese generally breed in small isolated groups.
annual cycle; Anser indicus; avian influenza; Bar-headed Goose; carbon; connectivity; deuterium; epidemiology; feather isotopes; molt
Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been utilized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution (500 m to 1 km) images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010–2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China—one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI), were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security.
phenology; flooding; transplanting; ripening; land use
Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.
Rice paddy; Cropland; Observation frequency; Data availability; Vegetation indices; Sanjiang Plain
Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.
Paddy rice fields; MODIS images; Land Surface Water Index (LSWI); Enhanced Vegetation Index (EVI); Land Surface Temperature (LST); Flooding; Northeastern China
Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.
Spatiotemporal data mining; animal movements; continuous behavior patterns
Poyang Lake is situated within the East Asian Flyway, a migratory corridor for waterfowl that also encompasses Guangdong Province, China, the epicenter of highly pathogenic avian influenza (HPAI) H5N1. The lake is the largest freshwater body in China and a significant congregation site for waterfowl; however, surrounding rice fields and poultry grazing have created an overlap with wild waterbirds, a situation conducive to avian influenza transmission. Reports of HPAI H5N1 in healthy wild ducks at Poyang Lake have raised concerns about the potential of resilient free-ranging birds to disseminate the virus. Yet the role wild ducks play in connecting regions of HPAI H5N1 outbreak in Asia is hindered by a lack of information about their migratory ecology. During 2007–08 we marked wild ducks at Poyang Lake with satellite transmitters to examine the location and timing of spring migration and identify any spatiotemporal relationship with HPAI H5N1 outbreaks. Species included the Eurasian wigeon (Anas penelope), northern pintail (Anas acuta), common teal (Anas crecca), falcated teal (Anas falcata), Baikal teal (Anas formosa), mallard (Anas platyrhynchos), garganey (Anas querquedula), and Chinese spotbill (Anas poecilohyncha). These wild ducks (excluding the resident mallard and Chinese spotbill ducks) followed the East Asian Flyway along the coast to breeding areas in northern China, eastern Mongolia, and eastern Russia. None migrated west toward Qinghai Lake (site of the largest wild bird epizootic), thus failing to demonstrate any migratory connection to the Central Asian Flyway. A newly developed Brownian bridge spatial analysis indicated that HPAI H5N1 outbreaks reported in the flyway were related to latitude and poultry density but not to the core migration corridor or to wetland habitats. Also, we found a temporal mismatch between timing of outbreaks and wild duck movements. These analyses depend on complete or representative reporting of outbreaks, but by documenting movements of wild waterfowl, we present ecological knowledge that better informs epidemiological investigations seeking to explain and predict the spread of avian influenza viruses.
Anatidae; waterfowl; Poyang Lake; Brownian bridge movement model; satellite telemetry; highly pathogenic avian influenza; H5N1; East Asian Flyway
Between March 2007 and July 2009, 325 Highly Pathogenic Avian Influenza (HPAI, subtype H5N1) outbreaks in poultry were reported in 154 out of a total of 486 sub-districts in Bangladesh. This study analyzed the temporal and spatial patterns of HPAI H5N1 outbreaks and quantified the relationship between several spatial risk factors and HPAI outbreaks in sub-districts in Bangladesh. We assessed spatial autocorrelation and spatial dependence, and identified clustering sub-districts with disease statistically similar to or dissimilar from their neighbors. Three significant risk factors associated to HPAI H5N1 virus outbreaks were identified; the quadratic log-transformation of human population density [humans per square kilometer, P = 0.01, OR 1.15 (95% CI: 1.03–1.28)], the log-transformation of the total commercial poultry population [number of commercial poultry per sub-district, P < 0.002, OR 1.40 (95% CI: 1.12–1.74)], and the number of roads per sub-district [P = 0.02, OR 1.07 (95% CI: 1.01–1.14)]. The distinct clusters of HPAI outbreaks and risk factors identified could assist the Government of Bangladesh to target surveillance and to concentrate response efforts in areas where disease is likely to occur. Concentrating response efforts may help to combat HPAI more effectively, reducing the environmental viral load and so reducing the number of disease incidents.
Highly Pathogenic Avian Influenza; Bangladesh; HPAI; H5N1; Risk factor; Spatial analysis
Thailand experienced several epidemic waves of the highly pathogenic avian influenza (HPAI) H5N1 between 2004 and 2005. This study investigated the role of water in the landscape, which has not been previously assessed because of a lack of high-resolution information on the distribution of flooded land at the time of the epidemic. Nine Landsat 7-Enhanced Thematic Mapper Plus scenes covering 174,610 km2 were processed using k-means unsupervised classification to map the distribution of flooded areas as well as permanent lakes and reservoirs at the time of the main epidemic HPAI H5N1 wave of October 2004. These variables, together with other factors previously identified as significantly associated with risk, were entered into an autologistic regression model in order to quantify the gain in risk explanation over previously published models. We found that, in addition to other factors previously identified as associated with risk, the proportion of land covered by flooding along with expansion of rivers and streams, derived from an existing, sub-district level (administrative level no. 3) geographical information system database, was a highly significant risk factor in this 2004 HPAI epidemic. These results suggest that water-borne transmission could have partly contributed to the spread of HPAI H5N1 during the epidemic. Future work stemming from these results should involve studies where the actual distribution of small canals, rivers, ponds, rice paddy fields and farms are mapped and tested against farm-level data with respect to HPAI H5N1.
highly pathogenic avian influenza; flooding; remote sensing; Landsat; Thailand
Tidal flats play a critical role in supporting biodiversity and in providing ecosystem services but are rapidly disappearing because of human activities. The Yangtze Estuary is one of the world’s largest alluvial estuaries and is adjacent to the most developed economic zone in China. Using the Yangtze Estuary as a study region, we developed an automatic algorithm to estimate tidal flat areas based on the Land Surface Water Index and the Normalized Difference Vegetation Index. The total area of tidal flats in the Yangtze Estuary has decreased by 36% over the past three decades, including a 38% reduction in saltmarshes and a 31% reduction in barren mudflats. Meanwhile, land claim has accumulated to 1077 km2, a value that exceeds the area of the remaining tidal flats. We divided the Yangtze Estuary into Shanghai and Jiangsu areas, which differ in riverine sediment supply and tidal flat management patterns. Although land claim has accelerated in both areas, the decline in tidal flat area has been much greater in Jiangsu than in Shanghai because of abundant supplies of sediment and artificial siltation in the latter area. The results highlight the need for better coastal planning and management based on tidal flat dynamics.
Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 106 km2. The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests.
Satellite-based tracking of migratory waterfowl is an important tool for understanding the potential role of wild birds in the long-distance transmission of highly pathogenic avian influenza. However, employing this technique on a continental scale is prohibitively expensive. This study explores the utility of stable isotope ratios in feathers in examining both the distances traveled by migratory birds and variation in migration behavior. We compared the satellite-derived movement data of 22 ducks from 8 species captured at wintering areas in Bangladesh, Turkey, and Hong Kong with deuterium ratios (δD) of these and other individuals captured at the same locations. We derived likely molting locations from the satellite tracking data and generated expected isotope ratios based on an interpolated map of δD in rainwater. Although δD was correlated with the distance between wintering and molting locations, surprisingly, measured δD values were not correlated with either expected values or latitudes of molting sites. However, population-level parameters derived from the satellite-tracking data, such as mean distance between wintering and molting locations and variation in migration distance, were reflected by means and variation of the stable isotope values. Our findings call into question the relevance of the rainfall isotope map for Asia for linking feather isotopes to molting locations, and underscore the need for extensive ground truthing in the form of feather-based isoscapes. Nevertheless, stable isotopes from feathers could inform disease models by characterizing the degree to which regional breeding populations interact at common wintering locations. Feather isotopes also could aid in surveying wintering locations to determine where high-resolution tracking techniques (e.g. satellite tracking) could most effectively be employed. Moreover, intrinsic markers such as stable isotopes offer the only means of inferring movement information from birds that have died as a result of infection. In the absence of feather based-isoscapes, we recommend a combination of isotope analysis and satellite-tracking as the best means of generating aggregate movement data for informing disease models.
geographical indicators; connectivity; disease modeling; epidemiology; disease vector; waterfowl; deuterium
As farmland systems vary over space and time (season and year), accurate and updated maps of paddy rice are needed for studies of food security and environmental problems. We selected a wheat-rice double-cropped area from fragmented landscapes along the rural–urban complex (Jiangsu Province, China) and explored the potential utility of integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) in mapping paddy rice planting areas. We first identified several main types of non-cropland land cover and then identified paddy rice fields by selecting pixels that were inundated only during paddy rice flooding periods. These key temporal windows were determined based on MODIS Land Surface Temperature and vegetation indices. The resultant paddy rice map was evaluated using regions of interest (ROIs) drawn from multiple high-resolution images, Google Earth, and in-situ cropland photos. The estimated overall accuracy and Kappa coefficient were 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map better detected changes in the paddy rice fields and revealed more details about their distribution. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems.
A major reservoir of Nipah virus is believed to be the flying fox genus Pteropus, a fruit bat distributed across many of the world’s tropical and sub-tropical areas. The emergence of the virus and its zoonotic transmission to livestock and humans have been linked to losses in the bat’s habitat. Nipah has been identified in a number of indigenous flying fox populations in Thailand. While no evidence of infection in domestic pigs or people has been found to date, pig farming is an active agricultural sector in Thailand and therefore could be a potential pathway for zoonotic disease transmission from the bat reservoirs. The disease, then, represents a potential zoonotic risk. To characterize the spatial habitat of flying fox populations along Thailand’s Central Plain, and to map potential contact zones between flying fox habitats, pig farms and human settlements, we conducted field observation, remote sensing, and ecological niche modeling to characterize flying fox colonies and their ecological neighborhoods. A Potential Surface Analysis was applied to map contact zones among local epizootic actors.
Flying fox colonies are found mainly on Thailand’s Central Plain, particularly in locations surrounded by bodies of water, vegetation, and safe havens such as Buddhist temples. High-risk areas for Nipah zoonosis in pigs include the agricultural ring around the Bangkok metropolitan region where the density of pig farms is high.
Passive and active surveillance programs should be prioritized around Bangkok, particularly on farms with low biosecurity, close to water, and/or on which orchards are concomitantly grown. Integration of human and animal health surveillance should be pursued in these same areas. Such proactive planning would help conserve flying fox colonies and should help prevent zoonotic transmission of Nipah and other pathogens.
Flying foxes; Nipah; Species distribution model; Ensemble modeling; Potential surface analysis
For decades, southern China has been considered to be an important source for emerging influenza viruses since key hosts live together in high densities in areas with intensive agriculture. However, the underlying conditions of emergence and spread of avian influenza viruses (AIV) have not been studied in detail, particularly the complex spatiotemporal interplay of viral transmission between wild and domestic ducks, two major actors of AIV epidemiology. In this synthesis, we examine the risks of avian influenza spread in Poyang Lake, an area of intensive free-ranging duck production and large numbers of wild waterfowl. Our synthesis shows that farming of free-grazing domestic ducks is intensive in this area and synchronized with wild duck migration. The presence of juvenile domestic ducks in harvested paddy fields prior to the arrival and departure of migrant ducks in the same fields may amplify the risk of AIV circulation and facilitate the transmission between wild and domestic populations. We provide evidence associating wild ducks migration with the spread of H5N1 in the spring of 2008 from southern China to South Korea, Russia, and Japan, supported by documented wild duck movements and phylogenetic analyses of highly pathogenic avian influenza H5N1 sequences. We suggest that prevention measures based on a modification of agricultural practices may be implemented in these areas to reduce the intensity of AIV transmission between wild and domestic ducks. This would require involving all local stakeholders to discuss feasible and acceptable solutions.
avian influenza virus; wild birds; migration; interface; contact; ecology; epidemiology; China; Poyang; telemetry; remote sensing; GPS
Identifying movement routes and stopover sites is necessary for developing effective management and conservation strategies for migratory animals. In the case of migratory birds, a collection of migration routes, known as a flyway, is often hundreds to thousands of kilometers long and can extend across political boundaries. Flyways encompass the entire geographic range between the breeding and non-breeding areas of a population, species, or a group of species, and they provide spatial frameworks for management and conservation across international borders. Existing flyway maps are largely qualitative accounts based on band returns and survey data rather than observed movement routes. In this study, we use satellite and GPS telemetry data and dynamic Brownian bridge movement models to build upon existing maps and describe waterfowl space use probabilistically in the Central Asian and East Asian-Australasian Flyways.
Our approach provided new information on migratory routes that was not easily attainable with existing methods to describe flyways. Utilization distributions from dynamic Brownian bridge movement models identified key staging and stopover sites, migration corridors and general flyway outlines in the Central Asian and East Asian-Australasian Flyways. A map of space use from ruddy shelducks depicted two separate movement corridors within the Central Asian Flyway, likely representing two distinct populations that show relatively strong connectivity between breeding and wintering areas. Bar-headed geese marked at seven locations in the Central Asian Flyway showed heaviest use at several stopover sites in the same general region of high-elevation lakes along the eastern Qinghai-Tibetan Plateau. Our analysis of data from multiple Anatidae species marked at sites throughout Asia highlighted major movement corridors across species and confirmed that the Central Asian and East Asian-Australasian Flyways were spatially distinct.
The dynamic Brownian bridge movement model improves our understanding of flyways by estimating relative use of regions in the flyway while providing detailed, quantitative information on migration timing and population connectivity including uncertainty between locations. This model effectively quantifies the relative importance of different migration corridors and stopover sites and may help prioritize specific areas in flyways for conservation of waterbird populations.
Electronic supplementary material
The online version of this article (doi:10.1186/s40462-015-0029-6) contains supplementary material, which is available to authorized users.
Dynamic Brownian bridge movement model; Flyways; Waterfowl; Migration; Stopover sites; Space use; Habitat conservation
Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled datasets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.
Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.
An avian influenza virus of the H7N9 type, associated with live-poultry markets, has caused two human epidemics in China. Here, the authors develop a statistical model that predicts the risk of H7N9 infection in live-poultry markets across Asia, as a tool for disease surveillance and control.
We investigated avian influenza infections in wild birds, poultry, and humans at Eastern Dongting Lake, China. We analyzed 6,621 environmental samples, including fresh fecal and water samples, from wild birds and domestic ducks that were collected from the Eastern Dongting Lake area from November 2011 to April 2012. We also conducted two cross-sectional serological studies in November 2011 and April 2012, with 1,050 serum samples collected from people exposed to wild birds and/or domestic ducks. Environmental samples were tested for the presence of avian influenza virus (AIV) using quantitative PCR assays and virus isolation techniques. Hemagglutination inhibition assays were used to detect antibodies against AIV H5N1, and microneutralization assays were used to confirm these results. Among the environmental samples from wild birds and domestic ducks, AIV prevalence was 5.19 and 5.32%, respectively. We isolated 39 and 5 AIVs from the fecal samples of wild birds and domestic ducks, respectively. Our analysis indicated 12 subtypes of AIV were present, suggesting that wild birds in the Eastern Dongting Lake area carried a diverse array of AIVs with low pathogenicity. We were unable to detect any antibodies against AIV H5N1 in humans, suggesting that human infection with H5N1 was rare in this region.
Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.
Highly pathogenic influenza A/H5N1 has persistently but sporadically caused human illness and death since 1997. Yet it is still unclear how this pathogen is able to persist globally. While wild birds seem to be a genetic reservoir for influenza A, they do not seem to be the main source of human illness. Here, we highlight the role that domestic poultry may play in maintaining A/H5N1 globally, using theoretical models of spatial population structure in poultry populations. We find that a metapopulation of moderately sized poultry flocks can sustain the pathogen in a finite poultry population for over two years. Our results suggest that it is possible that moderately intensive backyard farms could sustain the pathogen indefinitely in real systems. This fits a pattern that has been observed from many empirical systems. Rather than just employing standard culling procedures to control the disease, our model suggests ways that poultry production systems may be modified.
TOC summary: Reassortment is most likely to occur in eastern China, central China, or the Nile Delta in Egypt.
The 1957 and 1968 influenza pandemics, each of which killed ≈1 million persons, arose through reassortment events. Influenza virus in humans and domestic animals could reassort and cause another pandemic. To identify geographic areas where agricultural production systems are conducive to reassortment, we fitted multivariate regression models to surveillance data on influenza A virus subtype H5N1 among poultry in China and Egypt and subtype H3N2 among humans. We then applied the models across Asia and Egypt to predict where subtype H3N2 from humans and subtype H5N1 from birds overlap; this overlap serves as a proxy for co-infection and in vivo reassortment. For Asia, we refined the prioritization by identifying areas that also have high swine density. Potential geographic foci of reassortment include the northern plains of India, coastal and central provinces of China, the western Korean Peninsula and southwestern Japan in Asia, and the Nile Delta in Egypt.
influenza in birds; influenza A virus H3N2 subtype; influenza A virus H5N1 subtype; reassortant viruses; viruses; zoonoses; avian influenza; influenza